A billion dollar problem needs a solution right now!
So you bought clothes online, waited a few days, they arrive at your doorstep, nicely packaged in a box. And now you try them on, and they don’t fit well! How many times has it happened to you? Feels like every time, Right? The only option you have is to place them back in the box and send it back.
According to the industry reports, online fashion retailers get returns of around 20% — 40% of the garments sold online.
It seems like a huge inefficiency in online retail business. Not only that the ‘wrong’ product was shipped to customer, but the retailers have to bear the cost of bringing back the product. Then there is the cost of repacking them to be sold again.
Main culprit to this inefficiency is the wrong size ordered by the customer — knowingly or unknowingly. Many times, due to unstandardised sizes, it is difficult to find out your exact size in each brand. High returns, in this case, are inevitable. People order 2–3 sizes for trial and return the ones that don’t fit. The problem exists due to the non-standardised sizes among different brands.
Not all product returns are due to sizing issues though. There are other common reasons such as the difference in colour on screen and in real. Or that the customers didn’t like the fabric. Or they bought too many and are feeling guilty. Or they just changed their mind after the garments arrived home.
To understand the gravity of the problem, every retailer needs to carefully measure the returns and their reason. Only then they can solve the problem or put in place the right tools.
It is observed that almost 60% — 70% of the product returns happen due to the sizing issues. This makes the problem worth solving. Many retailers have expressed their need to implement a solution to solve the sizing issue and reduce their return rates.
It would be interesting to do a quick calculation to estimate the size of the problem:
In this case, assuming
i) Average sales value of an order = $50
ii) Total number of orders in a month = 100,000
iii) Shipping cost per order = $2.5 (Assuming average shipping cost = 5% of the sales value)
iv) Total orders returned in a month = 25,000 (Assuming a conservative figure of 25%)
v) Total sales = i *(ii — iv) = $50 * 75,000 = $3,750,000
Hence,
vi) Outward shipping cost = $2.5 * 100,000 = $250,000
vii) Return shipping cost = $2.5 * 25000 = $62,500
viii) Actual shipping cost = (vi + vii) = $312,500
ix) Shipping cost for 75,000 products ( iii * (ii — iv) )= $2.5 * 75,000 = $ 187,500
Inefficiency:
x) Extra shipping cost = $312,500 — $187,500 (vii — ix) = $125,000
xi) Extra Shipping cost (as % of total sales, (x/v)% ) = (125,000/3,750,000) = 3.3%
3.3%
This is the amount lost by a retailer from their profits, due to returns. That’s huge. To put things in perspective: the market size of global online fashion retail is $100 billion. So the online stores are losing $ 3.3 billion every year due to returns. Any solution in this market can make a huge impact. That’s enough motivation for you to work on this problem!
Retailers are divided on how to solve this problem. Some experts believe that the solution need not be a complex one. Just providing a lot of product information like product size, size of the model wearing a garment is enough to reduce the problem to a great extent. They believe that the technology can’t change the need of trials.
Others believe that technology is the key to solve this problem. It will enable the online shoppers to ‘try’ the garments without actually trying it on themselves. They believe that the technology might even enable physical stores to get rid of trial rooms. It will save space and stores can place more products instead. The customer will just walk in, get a 3D scan and in a second know what clothes would fit them well.
There are several startups working on solving this critical problem. Many of them have raised huge capital investment from large venture firms and investors.
- UK-based Fits.me raised $7.2 million series A round in 2013. It describes the clothes return as a “£7.4 billion problem globally”
- New York-based Clothes Horse and Stockholm-based Virtusize have raised undisclosed amounts of seed funding
- UK-based virtual fitting room start-up Metail raised about $4.4 million
- In March 2014, Ebay acquired the 3-D visualisation and modelling start-up Phisix
But, so far, nobody has been able to douse the fire completely.
The online retailers might not be able to pass on the cost of returns to the customers. A study conducted by Fits.me found that 60% customers don’t purchase clothing from the same retailer again, if they don’t offer free returns.
That’s why it is critical for an online retailer to pay attention to their actual return policy.
According to experts, a “high-adoption” solution to the fitting problem should be:
- Easy to use
- Low-friction
- User engaging
- cost-efficient to retailers
Existing Solutions
- Comparing with own garments
Virtusize allows customers to upload a picture of their own garments. Then it displays 2D silhouette of their garments with that of the garment that they intend to buy online. A customer can easily compare the size of a new garment with their own clothes and whether it would fit them.
It also allows you to compare your garments with products on various retailers that use Virtusize. You just have to register yourself with Virtusize and upload the garments that fit you well.
Virtusize also built a plugin for Shopify, the largest platform to build an online e-commerce store. This allows them to quickly gain traction among their target market. If you want to solve this problem, make sure you build a plugin for Shopify platform.
2. Recommendations using body measurements
Phisix is another popular tool. It scans and uploads hundreds of clothes by placing them on a graph paper, front and back, to measure their exact size. A customer can update her height, weight, and body measurements in Phisix. It will, then, match the customer body with thousands of mannequins. The mannequin will ‘wear’ the chosen garment to show how it would look on the customer. The mannequin can be shown in motion — like taking a golf shot — to know how the garment would perform in real life.
Other technologies working on this problem has similar approaches:
House of Fraser uses True Fit which asks for body measurements to recommend the best fitted size.
Sojeans has a tool called Soselect which offers personalised garment recommendations based on your body measurements, vague estimates and style preferences.
For most retailers, low-friction and easy to use solution is the ultimate priority. However, most of these solutions rely on user-provided data which is not an enjoyable shopping experience. Thus the adoption rates are quite low for such solutions.
Need for Low-friction/High-Adoption Solution
There is a need for a low-friction solution which doesn’t require any input from the users. Instead, it observes their past purchases to understand their body size and fit to recommend the right size and fit for them.
Remember, this is a multi-billion dollar problem which is always growing as more and more users are buying clothes online. And it needs a solution right now!
Machine learning to the rescue? We’ll leave it up to our smart makers to think about it!
How would you solve this problem?
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